hf_model_gemma – AI Model by aditi-rao-123 | AlphaNeural AI

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aditi-rao-123/
hf_model_gemma

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instruction-tuning

google/gemma-2-2b-it

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Gemma 2B Instruction LoRA Adapter (CPU Trained)

Model Description

This repository contains a LoRA (Low-Rank Adaptation) adapter fine-tuned on top of Google’s Gemma 2B Instruct model (google/gemma-2-2b-it).

The adapter was trained entirely on CPU using PEFT and TRL’s SFTTrainer for instruction-following tasks.

⚠️ This repository contains only LoRA adapter weights, not the full base model.

Model Details

Developed by: Aditi Rao
Shared by: Aditi Rao
Model type: Causal Language Model (LoRA Adapter)
Language(s): English
License: Gemma License
Finetuned from: google/gemma-2-2b-it

Training Details

Training Method

Fine-tuning approach: LoRA (PEFT)
Trainer: trl.SFTTrainer
Precision: float32
Hardware: CPU only

LoRA Configuration

Rank (r): 16
Alpha: 32
Dropout: 0.05
Target modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- up_proj
- down_proj

Training Hyperparameters

Epochs: 1
Batch size: 1
Gradient accumulation: 8
Learning rate: 1e-4
Max sequence length: 256
Optimizer: AdamW

Training Data

Custom instruction-style dataset in JSONL format
Chat-formatted using Gemma chat template
System messages normalized into user messages to comply with Gemma formatting rules

How to Get Started with the Model

Load the Adapter with the Base Model

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "google/gemma-2-2b-it"
adapter_id = "aditi-rao-123/hf_model_gemma"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float32,
    device_map="cpu"
)

model = PeftModel.from_pretrained(base_model, adapter_id)

prompt = "Explain what a cryptocurrency wallet is."
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))